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author | Nirjas Jakilim <Nirzak@users.noreply.github.com> | 2021-04-21 13:41:13 +0600 |
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committer | GitHub <noreply@github.com> | 2021-04-21 13:41:13 +0600 |
commit | c559bc8038ab684c68601757d2735c7be39befc4 (patch) | |
tree | d5be862cca739810d395993183f0e6a480978d41 /doc | |
parent | dabc5368c7a2dec101b1b52690e39e91c8f67bce (diff) | |
download | numpy-c559bc8038ab684c68601757d2735c7be39befc4.tar.gz |
Minor fix to add reference link to numpy.fill_diagonal function
Diffstat (limited to 'doc')
-rw-r--r-- | doc/source/user/tutorial-svd.rst | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst index fd9e366e0..7b905e51e 100644 --- a/doc/source/user/tutorial-svd.rst +++ b/doc/source/user/tutorial-svd.rst @@ -431,7 +431,7 @@ Now, to build our approximation, we first need to make sure that our singular values are ready for multiplication, so we build our ``Sigma`` matrix similarly to what we did before. The ``Sigma`` array must have dimensions ``(3, 768, 1024)``. In order to add the singular values to the diagonal of -``Sigma``, we will use the ``fill_diagonal`` function from NumPy, using each of +``Sigma``, we will use the `numpy.fill_diagonal` function from NumPy, using each of the 3 rows in ``s`` as the diagonal for each of the 3 matrices in ``Sigma``: :: |